{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6.用寻找到的最优参数训练模型,并在测试集上生成结果文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier\n",
    "import xgboost as xgb\n",
    "\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "from sklearn.metrics import log_loss\n",
    "\n",
    "from matplotlib import pyplot\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#读取训练数据\n",
    "train = pd.read_csv(\"RentListingInquries_FE_train.csv\")\n",
    "# 取出特征与标签\n",
    "y_train = train[\"interest_level\"].values\n",
    "train = train.drop([\"interest_level\"], axis=1)\n",
    "X_train = np.array(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#读取测试数据\n",
    "test = pd.read_csv(\"RentListingInquries_FE_test.csv\")\n",
    "\n",
    "X_test = np.array(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#使用前几步寻到的最优参数\n",
    "xgb1 = XGBClassifier(\n",
    "        learning_rate =0.1,\n",
    "        n_estimators=262,  \n",
    "        max_depth=5,\n",
    "        min_child_weight=1,\n",
    "        gamma=1,\n",
    "        subsample=0.8,\n",
    "        colsample_bytree=0.9,\n",
    "        colsample_bylevel=1,\n",
    "        reg_alpha=1.5,\n",
    "        reg_lambda=2,\n",
    "        objective= 'multi:softprob',  #在这里我有一个问题，softprob返回的是每类的概率，为何生成的结果文件直接给出了是哪一类\n",
    "        seed=3,\n",
    "        silent=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stdout",
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      "[16:34:37] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 48 extra nodes, 8 pruned nodes, max_depth=5\n",
      "[16:34:38] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 48 extra nodes, 4 pruned nodes, max_depth=5\n",
      "[16:34:38] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 38 extra nodes, 18 pruned nodes, max_depth=5\n",
      "[16:34:39] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 48 extra nodes, 14 pruned nodes, max_depth=5\n",
      "[16:34:39] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 42 extra nodes, 14 pruned nodes, max_depth=5\n",
      "[16:34:40] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 50 extra nodes, 6 pruned nodes, max_depth=5\n",
      "[16:34:41] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 38 extra nodes, 16 pruned nodes, max_depth=5\n",
      "[16:34:41] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 16 extra nodes, 4 pruned nodes, max_depth=5\n",
      "[16:34:42] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 28 extra nodes, 6 pruned nodes, max_depth=5\n",
      "[16:34:42] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 12 extra nodes, 6 pruned nodes, max_depth=5\n",
      "[16:34:43] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 54 extra nodes, 8 pruned nodes, max_depth=5\n",
      "[16:34:44] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 60 extra nodes, 2 pruned nodes, max_depth=5\n",
      "[16:34:44] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 50 extra nodes, 12 pruned nodes, max_depth=5\n",
      "[16:34:45] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 56 extra nodes, 6 pruned nodes, max_depth=5\n",
      "[16:34:45] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 30 extra nodes, 12 pruned nodes, max_depth=5\n",
      "[16:34:46] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 46 extra nodes, 16 pruned nodes, max_depth=5\n",
      "[16:34:47] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 50 extra nodes, 10 pruned nodes, max_depth=5\n",
      "[16:34:47] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 56 extra nodes, 6 pruned nodes, max_depth=5\n",
      "[16:34:48] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 38 extra nodes, 10 pruned nodes, max_depth=5\n",
      "[16:34:49] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 46 extra nodes, 8 pruned nodes, max_depth=5\n",
      "[16:34:49] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 40 extra nodes, 14 pruned nodes, max_depth=5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    }
   ],
   "source": [
    "#在测试集上测试，生成结果文件\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "#把网格搜索找到的最佳参数应用于训练集分类中：\n",
    "XGB1 = xgb1.fit(X_train, y_train)\n",
    "y_train_pred = XGB1.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification report for classifier XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
      "       colsample_bytree=0.9, gamma=1, learning_rate=0.1, max_delta_step=0,\n",
      "       max_depth=5, min_child_weight=1, missing=None, n_estimators=262,\n",
      "       n_jobs=1, nthread=None, objective='multi:softprob', random_state=0,\n",
      "       reg_alpha=1.5, reg_lambda=2, scale_pos_weight=1, seed=3, silent=0,\n",
      "       subsample=0.8):\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.75      0.40      0.52      3839\n",
      "          1       0.62      0.42      0.50     11229\n",
      "          2       0.82      0.95      0.88     34284\n",
      "\n",
      "avg / total       0.77      0.79      0.77     49352\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#训练集分类混淆矩阵\n",
    "print(\"Classification report for classifier %s:\\n%s\\n\"\n",
    "      % (XGB1, classification_report(y_train, y_train_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    }
   ],
   "source": [
    "#生成提交测试结果\n",
    "y_test_pred = XGB1.predict(X_test)\n",
    "\n",
    "df=pd.DataFrame(data = y_test_pred,columns = ['interest_level'])\n",
    "df.to_csv('result.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#测试结果可视化，与训练集的方形图三类比例大致相同\n",
    "sns.countplot(df.interest_level);\n",
    "pyplot.xlabel('interest_level');\n",
    "pyplot.ylabel('Number of occurrences');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 74659 entries, 0 to 74658\n",
      "Data columns (total 1 columns):\n",
      "interest_level    74659 non-null int64\n",
      "dtypes: int64(1)\n",
      "memory usage: 583.3 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
